Finally, all texture code \(f_s^i,i=1...K\)  are concatenated in channel-wise to get full texture code \(f_s\) . Different from [1]}, however,which use a avgpooling layer after each \(f_s^i\) , We modify the encoder network structure and cancel pooling, because we think pooling will cause a lot of information to be lost. What's more, in order to extract the feature of the texture code, [1]} uses a fully connected layer to extract the affine parameters required by the AdaIN layer, which will greatly increase the parameters of the network and reduce the efficiency of the network. On the contrary, we use a convolutional network as the fushion module, which makes the model more lightweight.
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